{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Ben Bagozzi\Desktop\Replication Files\Tables_A23_to_A24.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}16 Apr 2021, 08:10:09
{txt}
{com}. 
. *read-in monadic data
. use "MonMalariaDataset.dta"
{txt}
{com}. 
. *xtset data
. sort cowcode_2 year5
{txt}
{com}. xtset cowcode_2 year5
{res}{txt}{col 8}panel variable:  {res}cowcode_2 (unbalanced)
{txt}{col 9}time variable:  {res}{col 25}year5, 1 to 12
{txt}{col 17}delta:  {res}1 unit
{txt}
{com}. 
. *generate output for Table A.23
. summarize dr_1_at_2_m, detail

                      {txt}(sum) dr_1_at_2_m
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        4              0       {txt}Obs         {res}      1,624
{txt}25%    {res}       15              0       {txt}Sum of Wgt. {res}      1,624

{txt}50%    {res}       32                      {txt}Mean          {res} 37.57266
                        {txt}Largest       Std. Dev.     {res}  30.0079
{txt}75%    {res}       54            158
{txt}90%    {res}       77            161       {txt}Variance      {res} 900.4741
{txt}95%    {res}       99            169       {txt}Skewness      {res} 1.178917
{txt}99%    {res}      133            171       {txt}Kurtosis      {res} 4.553409
{txt}
{com}. 
. ********************************************************************************************************
. **Print log-likelihoods to screen for each Negative Binomial model (needed for LR tests further below)**
. ********************************************************************************************************
. 
. *Malaria only model
. nbreg dr_1_at_2_m 

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-23315.906}  
Iteration 1:{space 3}log likelihood = {res:-23315.906}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-7534.4955}  
Iteration 1:{space 3}log likelihood = {res:-7521.6439}  
Iteration 2:{space 3}log likelihood = {res:-7521.6101}  
Iteration 3:{space 3}log likelihood = {res:-7521.6101}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-7521.6101}  
Iteration 1:{space 3}log likelihood = {res:-7521.6101}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}     1,624
{txt}{col 49}LR chi2({res}0{txt}){col 67}= {res}      0.00
{txt}{col 1}Dispersion{col 16}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}         .
{txt}Log likelihood = {res}-7521.6101{txt}{col 49}Pseudo R2{col 67}= {res}    0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} dr_1_at_2_m{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} 3.626277{col 26}{space 2}     .023{col 37}{space 1}  157.66{col 46}{space 3}0.000{col 54}{space 4} 3.581198{col 67}{space 3} 3.671356
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
    /lnalpha {c |}{col 14}{res}{space 2}-.1833487{col 26}{space 2} .0355353{col 54}{space 4}-.2529966{col 67}{space 3}-.1137008
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} .8324778{col 26}{space 2} .0295823{col 54}{space 4} .7764705{col 67}{space 3} .8925249
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
LR test of alpha=0: {help j_chibar##|_new:chibar2(01) = }{res}3.2e+04{col 56}{txt}Prob >= chibar2 = {res}0.000
{txt}
{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}     1,624{col 27} -7521.61{col 38} -7521.61{col 49}     2{col 58} 15047.22{col 69} 15058.01
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. *Pooled model
. nbreg dr_1_at_2_m Lmalaria_interp_2_m Lcid_per_trop_2_m Llog_trade_m  Llog_gdppc_2_m Lln_cinc_2_m Ldemocracy_2_m Llog_distance_m t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12
{txt}note: t12 omitted because of collinearity

Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4875.3332}  
Iteration 1:{space 3}log likelihood = {res:-4875.2189}  
Iteration 2:{space 3}log likelihood = {res:-4875.2189}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-5193.1925}  
Iteration 1:{space 3}log likelihood = {res:-5023.6054}  
Iteration 2:{space 3}log likelihood = {res:-4998.2503}  
Iteration 3:{space 3}log likelihood = {res: -4998.226}  
Iteration 4:{space 3}log likelihood = {res: -4998.226}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4619.8153}  
Iteration 1:{space 3}log likelihood = {res:-4388.7454}  
Iteration 2:{space 3}log likelihood = {res:-4249.3123}  
Iteration 3:{space 3}log likelihood = {res: -4234.177}  
Iteration 4:{space 3}log likelihood = {res: -4234.072}  
Iteration 5:{space 3}log likelihood = {res: -4234.072}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}     1,072
{txt}{col 49}LR chi2({res}17{txt}){col 67}= {res}   1528.31
{txt}{col 1}Dispersion{col 16}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res} -4234.072{txt}{col 49}Pseudo R2{col 67}= {res}    0.1529

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        dr_1_at_2_m{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Lmalaria_interp_2_m {c |}{col 21}{res}{space 2}-.1604234{col 33}{space 2} .0393123{col 44}{space 1}   -4.08{col 53}{space 3}0.000{col 61}{space 4}-.2374741{col 74}{space 3}-.0833726
{txt}{space 2}Lcid_per_trop_2_m {c |}{col 21}{res}{space 2} .2177108{col 33}{space 2} .0349005{col 44}{space 1}    6.24{col 53}{space 3}0.000{col 61}{space 4}  .149307{col 74}{space 3} .2861145
{txt}{space 7}Llog_trade_m {c |}{col 21}{res}{space 2} .0008795{col 33}{space 2} .0000575{col 44}{space 1}   15.28{col 53}{space 3}0.000{col 61}{space 4} .0007667{col 74}{space 3} .0009923
{txt}{space 5}Llog_gdppc_2_m {c |}{col 21}{res}{space 2}  .048379{col 33}{space 2} .0170964{col 44}{space 1}    2.83{col 53}{space 3}0.005{col 61}{space 4} .0148706{col 74}{space 3} .0818873
{txt}{space 7}Lln_cinc_2_m {c |}{col 21}{res}{space 2} .1437586{col 33}{space 2} .0100647{col 44}{space 1}   14.28{col 53}{space 3}0.000{col 61}{space 4} .1240321{col 74}{space 3} .1634851
{txt}{space 5}Ldemocracy_2_m {c |}{col 21}{res}{space 2}-.0963175{col 33}{space 2} .0256149{col 44}{space 1}   -3.76{col 53}{space 3}0.000{col 61}{space 4}-.1465218{col 74}{space 3}-.0461132
{txt}{space 4}Llog_distance_m {c |}{col 21}{res}{space 2}-.2974826{col 33}{space 2} .0352762{col 44}{space 1}   -8.43{col 53}{space 3}0.000{col 61}{space 4}-.3666226{col 74}{space 3}-.2283426
{txt}{space 17}t2 {c |}{col 21}{res}{space 2} .2921639{col 33}{space 2} .1107445{col 44}{space 1}    2.64{col 53}{space 3}0.008{col 61}{space 4} .0751086{col 74}{space 3} .5092192
{txt}{space 17}t3 {c |}{col 21}{res}{space 2} .4440076{col 33}{space 2}  .069266{col 44}{space 1}    6.41{col 53}{space 3}0.000{col 61}{space 4} .3082487{col 74}{space 3} .5797666
{txt}{space 17}t4 {c |}{col 21}{res}{space 2} .4714409{col 33}{space 2} .0640361{col 44}{space 1}    7.36{col 53}{space 3}0.000{col 61}{space 4} .3459325{col 74}{space 3} .5969493
{txt}{space 17}t5 {c |}{col 21}{res}{space 2} .3850908{col 33}{space 2}  .053524{col 44}{space 1}    7.19{col 53}{space 3}0.000{col 61}{space 4} .2801858{col 74}{space 3} .4899958
{txt}{space 17}t6 {c |}{col 21}{res}{space 2} .2957107{col 33}{space 2} .0459023{col 44}{space 1}    6.44{col 53}{space 3}0.000{col 61}{space 4}  .205744{col 74}{space 3} .3856775
{txt}{space 17}t7 {c |}{col 21}{res}{space 2} .5443615{col 33}{space 2} .0417424{col 44}{space 1}   13.04{col 53}{space 3}0.000{col 61}{space 4}  .462548{col 74}{space 3}  .626175
{txt}{space 17}t8 {c |}{col 21}{res}{space 2}  .593823{col 33}{space 2} .0387346{col 44}{space 1}   15.33{col 53}{space 3}0.000{col 61}{space 4} .5179046{col 74}{space 3} .6697415
{txt}{space 17}t9 {c |}{col 21}{res}{space 2} .1424694{col 33}{space 2} .0387562{col 44}{space 1}    3.68{col 53}{space 3}0.000{col 61}{space 4} .0665087{col 74}{space 3} .2184302
{txt}{space 16}t10 {c |}{col 21}{res}{space 2} .1523999{col 33}{space 2} .0384478{col 44}{space 1}    3.96{col 53}{space 3}0.000{col 61}{space 4} .0770437{col 74}{space 3} .2277561
{txt}{space 16}t11 {c |}{col 21}{res}{space 2}  .157163{col 33}{space 2} .0371758{col 44}{space 1}    4.23{col 53}{space 3}0.000{col 61}{space 4} .0842998{col 74}{space 3} .2300262
{txt}{space 16}t12 {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (omitted)
{space 14}_cons {c |}{col 21}{res}{space 2} 5.489091{col 33}{space 2}  .321883{col 44}{space 1}   17.05{col 53}{space 3}0.000{col 61}{space 4} 4.858212{col 74}{space 3}  6.11997
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /lnalpha {c |}{col 21}{res}{space 2}-2.685198{col 33}{space 2} .0652197{col 61}{space 4}-2.813026{col 74}{space 3} -2.55737
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              alpha {c |}{col 21}{res}{space 2} .0682077{col 33}{space 2} .0044485{col 61}{space 4} .0600231{col 74}{space 3} .0775083
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
LR test of alpha=0: {help j_chibar##|_new:chibar2(01) = }{res}1282.29{col 56}{txt}Prob >= chibar2 = {res}0.000
{txt}
{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}     1,072{col 27}-4998.226{col 38}-4234.072{col 49}    19{col 58} 8506.144{col 69} 8600.712
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. *Pooled (clustered se's) model
. nbreg dr_1_at_2_m Lmalaria_interp_2_m Lcid_per_trop_2_m Llog_trade_m  Llog_gdppc_2_m Lln_cinc_2_m Ldemocracy_2_m Llog_distance_m t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12, cluster(cowcode_2)
{txt}note: t12 omitted because of collinearity

Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4875.3332}  
Iteration 1:{space 3}log pseudolikelihood = {res:-4875.2189}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4875.2189}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-5193.1925}  
Iteration 1:{space 3}log pseudolikelihood = {res:-5023.6054}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4998.2503}  
Iteration 3:{space 3}log pseudolikelihood = {res: -4998.226}  
Iteration 4:{space 3}log pseudolikelihood = {res: -4998.226}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4619.8153}  
Iteration 1:{space 3}log pseudolikelihood = {res:-4388.7454}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4249.3123}  
Iteration 3:{space 3}log pseudolikelihood = {res: -4234.177}  
Iteration 4:{space 3}log pseudolikelihood = {res: -4234.072}  
Iteration 5:{space 3}log pseudolikelihood = {res: -4234.072}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}     1,072
{txt}{col 49}Wald chi2({res}17{txt}){col 67}= {res}   1261.52
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -4234.072{txt}{col 49}Pseudo R2{col 67}= {res}    0.1529

{txt}{ralign 85:(Std. Err. adjusted for {res:153} clusters in cowcode_2)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}        dr_1_at_2_m{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Lmalaria_interp_2_m {c |}{col 21}{res}{space 2}-.1604234{col 33}{space 2} .0651702{col 44}{space 1}   -2.46{col 53}{space 3}0.014{col 61}{space 4}-.2881547{col 74}{space 3}-.0326921
{txt}{space 2}Lcid_per_trop_2_m {c |}{col 21}{res}{space 2} .2177108{col 33}{space 2} .0733494{col 44}{space 1}    2.97{col 53}{space 3}0.003{col 61}{space 4} .0739487{col 74}{space 3} .3614729
{txt}{space 7}Llog_trade_m {c |}{col 21}{res}{space 2} .0008795{col 33}{space 2} .0000946{col 44}{space 1}    9.29{col 53}{space 3}0.000{col 61}{space 4}  .000694{col 74}{space 3}  .001065
{txt}{space 5}Llog_gdppc_2_m {c |}{col 21}{res}{space 2}  .048379{col 33}{space 2} .0280597{col 44}{space 1}    1.72{col 53}{space 3}0.085{col 61}{space 4} -.006617{col 74}{space 3}  .103375
{txt}{space 7}Lln_cinc_2_m {c |}{col 21}{res}{space 2} .1437586{col 33}{space 2} .0172858{col 44}{space 1}    8.32{col 53}{space 3}0.000{col 61}{space 4} .1098791{col 74}{space 3} .1776382
{txt}{space 5}Ldemocracy_2_m {c |}{col 21}{res}{space 2}-.0963175{col 33}{space 2} .0364024{col 44}{space 1}   -2.65{col 53}{space 3}0.008{col 61}{space 4}-.1676648{col 74}{space 3}-.0249702
{txt}{space 4}Llog_distance_m {c |}{col 21}{res}{space 2}-.2974826{col 33}{space 2} .0710098{col 44}{space 1}   -4.19{col 53}{space 3}0.000{col 61}{space 4}-.4366594{col 74}{space 3}-.1583059
{txt}{space 17}t2 {c |}{col 21}{res}{space 2} .2921639{col 33}{space 2} .1190142{col 44}{space 1}    2.45{col 53}{space 3}0.014{col 61}{space 4} .0589004{col 74}{space 3} .5254274
{txt}{space 17}t3 {c |}{col 21}{res}{space 2} .4440076{col 33}{space 2}  .084284{col 44}{space 1}    5.27{col 53}{space 3}0.000{col 61}{space 4} .2788139{col 74}{space 3} .6092013
{txt}{space 17}t4 {c |}{col 21}{res}{space 2} .4714409{col 33}{space 2} .0757215{col 44}{space 1}    6.23{col 53}{space 3}0.000{col 61}{space 4} .3230294{col 74}{space 3} .6198524
{txt}{space 17}t5 {c |}{col 21}{res}{space 2} .3850908{col 33}{space 2} .0656118{col 44}{space 1}    5.87{col 53}{space 3}0.000{col 61}{space 4} .2564941{col 74}{space 3} .5136875
{txt}{space 17}t6 {c |}{col 21}{res}{space 2} .2957107{col 33}{space 2} .0569667{col 44}{space 1}    5.19{col 53}{space 3}0.000{col 61}{space 4} .1840581{col 74}{space 3} .4073634
{txt}{space 17}t7 {c |}{col 21}{res}{space 2} .5443615{col 33}{space 2} .0491537{col 44}{space 1}   11.07{col 53}{space 3}0.000{col 61}{space 4}  .448022{col 74}{space 3} .6407011
{txt}{space 17}t8 {c |}{col 21}{res}{space 2}  .593823{col 33}{space 2} .0423171{col 44}{space 1}   14.03{col 53}{space 3}0.000{col 61}{space 4} .5108831{col 74}{space 3}  .676763
{txt}{space 17}t9 {c |}{col 21}{res}{space 2} .1424694{col 33}{space 2} .0259895{col 44}{space 1}    5.48{col 53}{space 3}0.000{col 61}{space 4}  .091531{col 74}{space 3} .1934079
{txt}{space 16}t10 {c |}{col 21}{res}{space 2} .1523999{col 33}{space 2} .0246272{col 44}{space 1}    6.19{col 53}{space 3}0.000{col 61}{space 4} .1041314{col 74}{space 3} .2006684
{txt}{space 16}t11 {c |}{col 21}{res}{space 2}  .157163{col 33}{space 2} .0206503{col 44}{space 1}    7.61{col 53}{space 3}0.000{col 61}{space 4} .1166891{col 74}{space 3} .1976369
{txt}{space 16}t12 {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (omitted)
{space 14}_cons {c |}{col 21}{res}{space 2} 5.489091{col 33}{space 2} .5989557{col 44}{space 1}    9.16{col 53}{space 3}0.000{col 61}{space 4}  4.31516{col 74}{space 3} 6.663023
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /lnalpha {c |}{col 21}{res}{space 2}-2.685198{col 33}{space 2} .1533384{col 61}{space 4}-2.985736{col 74}{space 3}-2.384661
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              alpha {c |}{col 21}{res}{space 2} .0682077{col 33}{space 2} .0104589{col 61}{space 4} .0505023{col 74}{space 3} .0921202
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}     1,072{col 27}-4998.226{col 38}-4234.072{col 49}    19{col 58} 8506.144{col 69} 8600.712
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. *Receiver fixed effects model
. xtnbreg dr_1_at_2_m Lmalaria_interp_2_m Llog_trade_m Llog_gdppc_2_m Lln_cinc_2_m Ldemocracy_2_m t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12, fe
{txt}note: 19 groups (19 obs) dropped because of only one obs per group
note: t12 omitted because of collinearity
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-3283.4946}  
Iteration 1:{space 3}log likelihood = {res:-3276.0138}  
Iteration 2:{space 3}log likelihood = {res: -3275.997}  
Iteration 3:{space 3}log likelihood = {res: -3275.997}  
{res}
{txt}Conditional FE negative binomial regression{col 49}Number of obs{col 67}={col 69}{res}     1,066
{txt}Group variable: {res}cowcode_2{col 49}{txt}Number of groups{col 67}={col 69}{res}       136

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}       7.8
{txt}{col 63}max{col 67}={col 69}{res}        11

{txt}{col 49}Wald chi2({res}15{txt}){col 67}={col 70}{res}  1404.28
{txt}Log likelihood  = {res} -3275.997{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        dr_1_at_2_m{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Lmalaria_interp_2_m {c |}{col 21}{res}{space 2} -.097116{col 33}{space 2} .0469275{col 44}{space 1}   -2.07{col 53}{space 3}0.039{col 61}{space 4}-.1890923{col 74}{space 3}-.0051397
{txt}{space 7}Llog_trade_m {c |}{col 21}{res}{space 2} .0008679{col 33}{space 2} .0000821{col 44}{space 1}   10.58{col 53}{space 3}0.000{col 61}{space 4} .0007071{col 74}{space 3} .0010287
{txt}{space 5}Llog_gdppc_2_m {c |}{col 21}{res}{space 2}-.0390357{col 33}{space 2}  .035418{col 44}{space 1}   -1.10{col 53}{space 3}0.270{col 61}{space 4}-.1084538{col 74}{space 3} .0303824
{txt}{space 7}Lln_cinc_2_m {c |}{col 21}{res}{space 2} .0643617{col 33}{space 2} .0307685{col 44}{space 1}    2.09{col 53}{space 3}0.036{col 61}{space 4} .0040566{col 74}{space 3} .1246669
{txt}{space 5}Ldemocracy_2_m {c |}{col 21}{res}{space 2}-.0469268{col 33}{space 2} .0305578{col 44}{space 1}   -1.54{col 53}{space 3}0.125{col 61}{space 4}-.1068191{col 74}{space 3} .0129654
{txt}{space 17}t2 {c |}{col 21}{res}{space 2} .2405379{col 33}{space 2} .1191346{col 44}{space 1}    2.02{col 53}{space 3}0.043{col 61}{space 4} .0070385{col 74}{space 3} .4740374
{txt}{space 17}t3 {c |}{col 21}{res}{space 2} .3124334{col 33}{space 2}  .088077{col 44}{space 1}    3.55{col 53}{space 3}0.000{col 61}{space 4} .1398056{col 74}{space 3} .4850613
{txt}{space 17}t4 {c |}{col 21}{res}{space 2} .3349416{col 33}{space 2} .0812953{col 44}{space 1}    4.12{col 53}{space 3}0.000{col 61}{space 4} .1756058{col 74}{space 3} .4942774
{txt}{space 17}t5 {c |}{col 21}{res}{space 2} .2828581{col 33}{space 2} .0684938{col 44}{space 1}    4.13{col 53}{space 3}0.000{col 61}{space 4} .1486128{col 74}{space 3} .4171034
{txt}{space 17}t6 {c |}{col 21}{res}{space 2} .2134912{col 33}{space 2} .0560637{col 44}{space 1}    3.81{col 53}{space 3}0.000{col 61}{space 4} .1036083{col 74}{space 3} .3233741
{txt}{space 17}t7 {c |}{col 21}{res}{space 2} .4557294{col 33}{space 2}  .044468{col 44}{space 1}   10.25{col 53}{space 3}0.000{col 61}{space 4} .3685738{col 74}{space 3} .5428851
{txt}{space 17}t8 {c |}{col 21}{res}{space 2} .4823862{col 33}{space 2} .0330541{col 44}{space 1}   14.59{col 53}{space 3}0.000{col 61}{space 4} .4176015{col 74}{space 3}  .547171
{txt}{space 17}t9 {c |}{col 21}{res}{space 2} .1018387{col 33}{space 2} .0341115{col 44}{space 1}    2.99{col 53}{space 3}0.003{col 61}{space 4} .0349814{col 74}{space 3}  .168696
{txt}{space 16}t10 {c |}{col 21}{res}{space 2} .1089072{col 33}{space 2}    .0332{col 44}{space 1}    3.28{col 53}{space 3}0.001{col 61}{space 4} .0438364{col 74}{space 3} .1739781
{txt}{space 16}t11 {c |}{col 21}{res}{space 2} .1215428{col 33}{space 2} .0295812{col 44}{space 1}    4.11{col 53}{space 3}0.000{col 61}{space 4} .0635647{col 74}{space 3} .1795209
{txt}{space 16}t12 {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (omitted)
{space 14}_cons {c |}{col 21}{res}{space 2} 3.240459{col 33}{space 2} .3354195{col 44}{space 1}    9.66{col 53}{space 3}0.000{col 61}{space 4} 2.583049{col 74}{space 3} 3.897869
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}     1,066{col 27}        .{col 38}-3275.997{col 49}    16{col 58} 6583.994{col 69} 6663.541
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. *Receiver random effects model
. xtnbreg dr_1_at_2_m Lmalaria_interp_2_m Lcid_per_trop_2_m Llog_trade_m Llog_gdppc_2_m Lln_cinc_2_m Ldemocracy_2_m Llog_distance_m t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12, i(cowcode_2)
{txt}note: t12 omitted because of collinearity

Fitting negative binomial (constant dispersion) model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4875.3332}  
Iteration 1:{space 3}log likelihood = {res:-4875.2189}  
Iteration 2:{space 3}log likelihood = {res:-4875.2189}  
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-8040.7755}  
Iteration 1:{space 3}log likelihood = {res:-6033.6555}  
Iteration 2:{space 3}log likelihood = {res:-5807.0194}  
Iteration 3:{space 3}log likelihood = {res:-5001.0743}  
Iteration 4:{space 3}log likelihood = {res:-4998.2286}  
Iteration 5:{space 3}log likelihood = {res: -4998.226}  
Iteration 6:{space 3}log likelihood = {res: -4998.226}  
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -4998.226}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-4698.5585}  
Iteration 2:{space 3}log likelihood = {res:-4225.0675}  
Iteration 3:{space 3}log likelihood = {res:-4188.9931}  
Iteration 4:{space 3}log likelihood = {res:-4186.3526}  
Iteration 5:{space 3}log likelihood = {res:-4186.3477}  
Iteration 6:{space 3}log likelihood = {res:-4186.3477}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4261.5363}  
Iteration 1:{space 3}log likelihood = {res:-4095.3361}  
Iteration 2:{space 3}log likelihood = {res:-4092.3706}  
Iteration 3:{space 3}log likelihood = {res: -4067.613}  
Iteration 4:{space 3}log likelihood = {res:-4053.9854}  
Iteration 5:{space 3}log likelihood = {res:-4053.3425}  
Iteration 6:{space 3}log likelihood = {res:-4053.3419}  
Iteration 7:{space 3}log likelihood = {res:-4053.3419}  
{res}
{txt}Random-effects negative binomial regression{col 49}Number of obs{col 67}={col 69}{res}     1,072
{txt}Group variable: {res}cowcode_2{col 49}{txt}Number of groups{col 67}={col 69}{res}       153

{txt}Random effects u_i ~ {res}Beta{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}       7.0
{txt}{col 63}max{col 67}={col 69}{res}        11

{txt}{col 49}Wald chi2({res}17{txt}){col 67}={col 70}{res}  2305.21
{txt}Log likelihood  = {res}-4053.3419{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        dr_1_at_2_m{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Lmalaria_interp_2_m {c |}{col 21}{res}{space 2}-.1190249{col 33}{space 2} .0427589{col 44}{space 1}   -2.78{col 53}{space 3}0.005{col 61}{space 4}-.2028308{col 74}{space 3}-.0352189
{txt}{space 2}Lcid_per_trop_2_m {c |}{col 21}{res}{space 2} .2528216{col 33}{space 2} .0564497{col 44}{space 1}    4.48{col 53}{space 3}0.000{col 61}{space 4} .1421822{col 74}{space 3} .3634609
{txt}{space 7}Llog_trade_m {c |}{col 21}{res}{space 2} .0009223{col 33}{space 2} .0000614{col 44}{space 1}   15.02{col 53}{space 3}0.000{col 61}{space 4} .0008019{col 74}{space 3} .0010426
{txt}{space 5}Llog_gdppc_2_m {c |}{col 21}{res}{space 2} .0242937{col 33}{space 2} .0241272{col 44}{space 1}    1.01{col 53}{space 3}0.314{col 61}{space 4}-.0229947{col 74}{space 3} .0715821
{txt}{space 7}Lln_cinc_2_m {c |}{col 21}{res}{space 2} .1547551{col 33}{space 2} .0139667{col 44}{space 1}   11.08{col 53}{space 3}0.000{col 61}{space 4} .1273808{col 74}{space 3} .1821293
{txt}{space 5}Ldemocracy_2_m {c |}{col 21}{res}{space 2}-.0557574{col 33}{space 2} .0275304{col 44}{space 1}   -2.03{col 53}{space 3}0.043{col 61}{space 4}-.1097161{col 74}{space 3}-.0017988
{txt}{space 4}Llog_distance_m {c |}{col 21}{res}{space 2}-.3735097{col 33}{space 2} .0595212{col 44}{space 1}   -6.28{col 53}{space 3}0.000{col 61}{space 4}-.4901691{col 74}{space 3}-.2568504
{txt}{space 17}t2 {c |}{col 21}{res}{space 2}  .355455{col 33}{space 2} .1023137{col 44}{space 1}    3.47{col 53}{space 3}0.001{col 61}{space 4} .1549238{col 74}{space 3} .5559862
{txt}{space 17}t3 {c |}{col 21}{res}{space 2} .4288217{col 33}{space 2} .0697283{col 44}{space 1}    6.15{col 53}{space 3}0.000{col 61}{space 4} .2921567{col 74}{space 3} .5654866
{txt}{space 17}t4 {c |}{col 21}{res}{space 2} .4436907{col 33}{space 2} .0639392{col 44}{space 1}    6.94{col 53}{space 3}0.000{col 61}{space 4} .3183723{col 74}{space 3} .5690092
{txt}{space 17}t5 {c |}{col 21}{res}{space 2} .3697662{col 33}{space 2}  .052797{col 44}{space 1}    7.00{col 53}{space 3}0.000{col 61}{space 4}  .266286{col 74}{space 3} .4732463
{txt}{space 17}t6 {c |}{col 21}{res}{space 2} .2951176{col 33}{space 2} .0429805{col 44}{space 1}    6.87{col 53}{space 3}0.000{col 61}{space 4} .2108774{col 74}{space 3} .3793579
{txt}{space 17}t7 {c |}{col 21}{res}{space 2} .5209791{col 33}{space 2} .0363051{col 44}{space 1}   14.35{col 53}{space 3}0.000{col 61}{space 4} .4498225{col 74}{space 3} .5921357
{txt}{space 17}t8 {c |}{col 21}{res}{space 2} .5234786{col 33}{space 2} .0294738{col 44}{space 1}   17.76{col 53}{space 3}0.000{col 61}{space 4} .4657111{col 74}{space 3} .5812462
{txt}{space 17}t9 {c |}{col 21}{res}{space 2} .1523727{col 33}{space 2} .0303857{col 44}{space 1}    5.01{col 53}{space 3}0.000{col 61}{space 4} .0928178{col 74}{space 3} .2119276
{txt}{space 16}t10 {c |}{col 21}{res}{space 2} .1631177{col 33}{space 2} .0300044{col 44}{space 1}    5.44{col 53}{space 3}0.000{col 61}{space 4} .1043103{col 74}{space 3} .2219252
{txt}{space 16}t11 {c |}{col 21}{res}{space 2} .1675475{col 33}{space 2} .0275776{col 44}{space 1}    6.08{col 53}{space 3}0.000{col 61}{space 4} .1134965{col 74}{space 3} .2215986
{txt}{space 16}t12 {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (omitted)
{space 14}_cons {c |}{col 21}{res}{space 2} 6.116129{col 33}{space 2} .5417217{col 44}{space 1}   11.29{col 53}{space 3}0.000{col 61}{space 4} 5.054374{col 74}{space 3} 7.177884
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /ln_r {c |}{col 21}{res}{space 2}  3.71861{col 33}{space 2} .1598616{col 61}{space 4} 3.405287{col 74}{space 3} 4.031933
{txt}              /ln_s {c |}{col 21}{res}{space 2} 3.858004{col 33}{space 2} .1775571{col 61}{space 4} 3.509999{col 74}{space 3}  4.20601
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                  r {c |}{col 21}{res}{space 2} 41.20707{col 33}{space 2} 6.587428{col 61}{space 4} 30.12294{col 74}{space 3} 56.36976
{txt}                  s {c |}{col 21}{res}{space 2} 47.37071{col 33}{space 2} 8.411005{col 61}{space 4} 33.44823{col 74}{space 3}  67.0883
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. pooled: {help j_chibar##|_new:chibar2(01) = }{res}266.01{col 56}{txt}Prob >= chibar2 = {res}0.000
{txt}
{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}     1,072{col 27}        .{col 38}-4053.342{col 49}    20{col 58} 8146.684{col 69} 8246.229
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. **********************************************************************************************
. **Print log-likelihoods to screen for each Poisson model (needed for LR tests further below)**
. **********************************************************************************************
. 
. *Malaria only model
. poisson dr_1_at_2_m 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-23315.906}  
Iteration 1:{space 3}log likelihood = {res:-23315.906}  
{res}
{txt}Poisson regression{col 49}Number of obs{col 67}= {res}     1,624
{txt}{col 49}LR chi2({res}0{txt}){col 67}= {res}      0.00
{txt}{col 49}Prob > chi2{col 67}= {res}         .
{txt}Log likelihood = {res}-23315.906{txt}{col 49}Pseudo R2{col 67}= {res}    0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} dr_1_at_2_m{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} 3.626277{col 26}{space 2} .0040483{col 37}{space 1}  895.76{col 46}{space 3}0.000{col 54}{space 4} 3.618342{col 67}{space 3} 3.634211
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}     1,624{col 27}-23315.91{col 38}-23315.91{col 49}     1{col 58} 46633.81{col 69}  46639.2
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. *Pooled model
. poisson dr_1_at_2_m Lmalaria_interp_2_m Lcid_per_trop_2_m Llog_trade_m  Llog_gdppc_2_m Lln_cinc_2_m Ldemocracy_2_m Llog_distance_m t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12
{txt}note: t12 omitted because of collinearity
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4875.3332}  
Iteration 1:{space 3}log likelihood = {res:-4875.2189}  
Iteration 2:{space 3}log likelihood = {res:-4875.2189}  
{res}
{txt}Poisson regression{col 49}Number of obs{col 67}= {res}     1,072
{txt}{col 49}LR chi2({res}17{txt}){col 67}= {res}  14796.75
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-4875.2189{txt}{col 49}Pseudo R2{col 67}= {res}    0.6028

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        dr_1_at_2_m{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Lmalaria_interp_2_m {c |}{col 21}{res}{space 2}-.1517991{col 33}{space 2}  .020323{col 44}{space 1}   -7.47{col 53}{space 3}0.000{col 61}{space 4}-.1916314{col 74}{space 3}-.1119668
{txt}{space 2}Lcid_per_trop_2_m {c |}{col 21}{res}{space 2} .1782795{col 33}{space 2} .0179717{col 44}{space 1}    9.92{col 53}{space 3}0.000{col 61}{space 4} .1430556{col 74}{space 3} .2135035
{txt}{space 7}Llog_trade_m {c |}{col 21}{res}{space 2} .0008068{col 33}{space 2} .0000281{col 44}{space 1}   28.66{col 53}{space 3}0.000{col 61}{space 4} .0007516{col 74}{space 3} .0008619
{txt}{space 5}Llog_gdppc_2_m {c |}{col 21}{res}{space 2} .0427362{col 33}{space 2} .0083717{col 44}{space 1}    5.10{col 53}{space 3}0.000{col 61}{space 4} .0263279{col 74}{space 3} .0591445
{txt}{space 7}Lln_cinc_2_m {c |}{col 21}{res}{space 2}  .137687{col 33}{space 2} .0050002{col 44}{space 1}   27.54{col 53}{space 3}0.000{col 61}{space 4} .1278868{col 74}{space 3} .1474871
{txt}{space 5}Ldemocracy_2_m {c |}{col 21}{res}{space 2}-.0948573{col 33}{space 2} .0123427{col 44}{space 1}   -7.69{col 53}{space 3}0.000{col 61}{space 4}-.1190486{col 74}{space 3}-.0706659
{txt}{space 4}Llog_distance_m {c |}{col 21}{res}{space 2} -.320894{col 33}{space 2}  .017085{col 44}{space 1}  -18.78{col 53}{space 3}0.000{col 61}{space 4}-.3543801{col 74}{space 3}-.2874079
{txt}{space 17}t2 {c |}{col 21}{res}{space 2} .2349377{col 33}{space 2} .0598676{col 44}{space 1}    3.92{col 53}{space 3}0.000{col 61}{space 4} .1175994{col 74}{space 3} .3522761
{txt}{space 17}t3 {c |}{col 21}{res}{space 2} .3573763{col 33}{space 2}  .037294{col 44}{space 1}    9.58{col 53}{space 3}0.000{col 61}{space 4} .2842814{col 74}{space 3} .4304712
{txt}{space 17}t4 {c |}{col 21}{res}{space 2} .3765519{col 33}{space 2} .0341271{col 44}{space 1}   11.03{col 53}{space 3}0.000{col 61}{space 4} .3096639{col 74}{space 3} .4434399
{txt}{space 17}t5 {c |}{col 21}{res}{space 2} .3056791{col 33}{space 2} .0282444{col 44}{space 1}   10.82{col 53}{space 3}0.000{col 61}{space 4} .2503211{col 74}{space 3} .3610371
{txt}{space 17}t6 {c |}{col 21}{res}{space 2} .2639992{col 33}{space 2}  .023651{col 44}{space 1}   11.16{col 53}{space 3}0.000{col 61}{space 4} .2176441{col 74}{space 3} .3103543
{txt}{space 17}t7 {c |}{col 21}{res}{space 2} .4828675{col 33}{space 2}  .020289{col 44}{space 1}   23.80{col 53}{space 3}0.000{col 61}{space 4} .4431017{col 74}{space 3} .5226332
{txt}{space 17}t8 {c |}{col 21}{res}{space 2}  .497108{col 33}{space 2} .0176836{col 44}{space 1}   28.11{col 53}{space 3}0.000{col 61}{space 4} .4624487{col 74}{space 3} .5317673
{txt}{space 17}t9 {c |}{col 21}{res}{space 2} .1231113{col 33}{space 2}  .018833{col 44}{space 1}    6.54{col 53}{space 3}0.000{col 61}{space 4} .0861993{col 74}{space 3} .1600234
{txt}{space 16}t10 {c |}{col 21}{res}{space 2} .1392339{col 33}{space 2} .0186489{col 44}{space 1}    7.47{col 53}{space 3}0.000{col 61}{space 4} .1026828{col 74}{space 3}  .175785
{txt}{space 16}t11 {c |}{col 21}{res}{space 2} .1557687{col 33}{space 2} .0176013{col 44}{space 1}    8.85{col 53}{space 3}0.000{col 61}{space 4} .1212709{col 74}{space 3} .1902666
{txt}{space 16}t12 {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (omitted)
{space 14}_cons {c |}{col 21}{res}{space 2}  5.82477{col 33}{space 2} .1586376{col 44}{space 1}   36.72{col 53}{space 3}0.000{col 61}{space 4} 5.513846{col 74}{space 3} 6.135694
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}     1,072{col 27}-12273.59{col 38}-4875.219{col 49}    18{col 58} 9786.438{col 69} 9876.029
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. *Pooled (clustered se's) model
. poisson dr_1_at_2_m Lmalaria_interp_2_m Lcid_per_trop_2_m Llog_trade_m  Llog_gdppc_2_m Lln_cinc_2_m Ldemocracy_2_m Llog_distance_m t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12, cluster(cowcode_2)
{txt}note: t12 omitted because of collinearity
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4875.3332}  
Iteration 1:{space 3}log pseudolikelihood = {res:-4875.2189}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4875.2189}  
{res}
{txt}Poisson regression{col 49}Number of obs{col 67}= {res}     1,072
{txt}{col 49}Wald chi2({res}17{txt}){col 67}= {res}   1676.33
{txt}Log pseudolikelihood = {res}-4875.2189{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:153} clusters in cowcode_2)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}        dr_1_at_2_m{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Lmalaria_interp_2_m {c |}{col 21}{res}{space 2}-.1517991{col 33}{space 2} .0607218{col 44}{space 1}   -2.50{col 53}{space 3}0.012{col 61}{space 4}-.2708117{col 74}{space 3}-.0327865
{txt}{space 2}Lcid_per_trop_2_m {c |}{col 21}{res}{space 2} .1782795{col 33}{space 2}  .065365{col 44}{space 1}    2.73{col 53}{space 3}0.006{col 61}{space 4} .0501665{col 74}{space 3} .3063926
{txt}{space 7}Llog_trade_m {c |}{col 21}{res}{space 2} .0008068{col 33}{space 2} .0000799{col 44}{space 1}   10.10{col 53}{space 3}0.000{col 61}{space 4} .0006502{col 74}{space 3} .0009633
{txt}{space 5}Llog_gdppc_2_m {c |}{col 21}{res}{space 2} .0427362{col 33}{space 2} .0249313{col 44}{space 1}    1.71{col 53}{space 3}0.086{col 61}{space 4}-.0061282{col 74}{space 3} .0916007
{txt}{space 7}Lln_cinc_2_m {c |}{col 21}{res}{space 2}  .137687{col 33}{space 2} .0153381{col 44}{space 1}    8.98{col 53}{space 3}0.000{col 61}{space 4} .1076249{col 74}{space 3}  .167749
{txt}{space 5}Ldemocracy_2_m {c |}{col 21}{res}{space 2}-.0948573{col 33}{space 2} .0335954{col 44}{space 1}   -2.82{col 53}{space 3}0.005{col 61}{space 4} -.160703{col 74}{space 3}-.0290115
{txt}{space 4}Llog_distance_m {c |}{col 21}{res}{space 2} -.320894{col 33}{space 2} .0642326{col 44}{space 1}   -5.00{col 53}{space 3}0.000{col 61}{space 4}-.4467877{col 74}{space 3}-.1950004
{txt}{space 17}t2 {c |}{col 21}{res}{space 2} .2349377{col 33}{space 2} .1120544{col 44}{space 1}    2.10{col 53}{space 3}0.036{col 61}{space 4} .0153151{col 74}{space 3} .4545603
{txt}{space 17}t3 {c |}{col 21}{res}{space 2} .3573763{col 33}{space 2} .0768276{col 44}{space 1}    4.65{col 53}{space 3}0.000{col 61}{space 4}  .206797{col 74}{space 3} .5079557
{txt}{space 17}t4 {c |}{col 21}{res}{space 2} .3765519{col 33}{space 2} .0699502{col 44}{space 1}    5.38{col 53}{space 3}0.000{col 61}{space 4}  .239452{col 74}{space 3} .5136517
{txt}{space 17}t5 {c |}{col 21}{res}{space 2} .3056791{col 33}{space 2} .0602689{col 44}{space 1}    5.07{col 53}{space 3}0.000{col 61}{space 4} .1875543{col 74}{space 3} .4238039
{txt}{space 17}t6 {c |}{col 21}{res}{space 2} .2639992{col 33}{space 2} .0513293{col 44}{space 1}    5.14{col 53}{space 3}0.000{col 61}{space 4} .1633957{col 74}{space 3} .3646028
{txt}{space 17}t7 {c |}{col 21}{res}{space 2} .4828675{col 33}{space 2} .0455313{col 44}{space 1}   10.61{col 53}{space 3}0.000{col 61}{space 4} .3936278{col 74}{space 3} .5721072
{txt}{space 17}t8 {c |}{col 21}{res}{space 2}  .497108{col 33}{space 2} .0375697{col 44}{space 1}   13.23{col 53}{space 3}0.000{col 61}{space 4} .4234727{col 74}{space 3} .5707433
{txt}{space 17}t9 {c |}{col 21}{res}{space 2} .1231113{col 33}{space 2} .0251931{col 44}{space 1}    4.89{col 53}{space 3}0.000{col 61}{space 4} .0737338{col 74}{space 3} .1724889
{txt}{space 16}t10 {c |}{col 21}{res}{space 2} .1392339{col 33}{space 2} .0238031{col 44}{space 1}    5.85{col 53}{space 3}0.000{col 61}{space 4} .0925806{col 74}{space 3} .1858871
{txt}{space 16}t11 {c |}{col 21}{res}{space 2} .1557687{col 33}{space 2} .0204439{col 44}{space 1}    7.62{col 53}{space 3}0.000{col 61}{space 4} .1156994{col 74}{space 3} .1958381
{txt}{space 16}t12 {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (omitted)
{space 14}_cons {c |}{col 21}{res}{space 2}  5.82477{col 33}{space 2}  .561877{col 44}{space 1}   10.37{col 53}{space 3}0.000{col 61}{space 4} 4.723512{col 74}{space 3} 6.926029
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}     1,072{col 27}        .{col 38}-4875.219{col 49}    18{col 58} 9786.438{col 69} 9876.029
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. *Receiver fixed effects model
. xtpoisson dr_1_at_2_m Lmalaria_interp_2_m Llog_trade_m Llog_gdppc_2_m Lln_cinc_2_m Ldemocracy_2_m t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12, fe
{txt}note: 19 groups (19 obs) dropped because of only one obs per group
note: t12 omitted because of collinearity
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4905.2127}  
Iteration 1:{space 3}log likelihood = {res:-3441.8823}  
Iteration 2:{space 3}log likelihood = {res:-3420.1697}  
Iteration 3:{space 3}log likelihood = {res:-3420.1555}  
Iteration 4:{space 3}log likelihood = {res:-3420.1555}  
{res}
{txt}Conditional fixed-effects Poisson regression{col 49}Number of obs{col 67}={col 69}{res}     1,066
{txt}Group variable: {res}cowcode_2{col 49}{txt}Number of groups{col 67}={col 69}{res}       136

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}       7.8
{txt}{col 63}max{col 67}={col 69}{res}        11

{txt}{col 49}Wald chi2({res}15{txt}){col 67}={col 70}{res}  2804.84
{txt}Log likelihood  = {res}-3420.1555{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        dr_1_at_2_m{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Lmalaria_interp_2_m {c |}{col 21}{res}{space 2}-.0690807{col 33}{space 2} .0320937{col 44}{space 1}   -2.15{col 53}{space 3}0.031{col 61}{space 4}-.1319832{col 74}{space 3}-.0061782
{txt}{space 7}Llog_trade_m {c |}{col 21}{res}{space 2} .0008544{col 33}{space 2} .0000581{col 44}{space 1}   14.71{col 53}{space 3}0.000{col 61}{space 4} .0007405{col 74}{space 3} .0009683
{txt}{space 5}Llog_gdppc_2_m {c |}{col 21}{res}{space 2}-.0785453{col 33}{space 2} .0254682{col 44}{space 1}   -3.08{col 53}{space 3}0.002{col 61}{space 4} -.128462{col 74}{space 3}-.0286286
{txt}{space 7}Lln_cinc_2_m {c |}{col 21}{res}{space 2} .0864005{col 33}{space 2} .0209389{col 44}{space 1}    4.13{col 53}{space 3}0.000{col 61}{space 4} .0453611{col 74}{space 3} .1274399
{txt}{space 5}Ldemocracy_2_m {c |}{col 21}{res}{space 2}-.0580993{col 33}{space 2} .0206029{col 44}{space 1}   -2.82{col 53}{space 3}0.005{col 61}{space 4}-.0984803{col 74}{space 3}-.0177184
{txt}{space 17}t2 {c |}{col 21}{res}{space 2} .1529082{col 33}{space 2}  .082275{col 44}{space 1}    1.86{col 53}{space 3}0.063{col 61}{space 4}-.0083479{col 74}{space 3} .3141643
{txt}{space 17}t3 {c |}{col 21}{res}{space 2} .2517752{col 33}{space 2} .0623076{col 44}{space 1}    4.04{col 53}{space 3}0.000{col 61}{space 4} .1296546{col 74}{space 3} .3738958
{txt}{space 17}t4 {c |}{col 21}{res}{space 2} .2836156{col 33}{space 2} .0580175{col 44}{space 1}    4.89{col 53}{space 3}0.000{col 61}{space 4} .1699034{col 74}{space 3} .3973278
{txt}{space 17}t5 {c |}{col 21}{res}{space 2} .2483572{col 33}{space 2}  .048952{col 44}{space 1}    5.07{col 53}{space 3}0.000{col 61}{space 4} .1524131{col 74}{space 3} .3443014
{txt}{space 17}t6 {c |}{col 21}{res}{space 2} .2091936{col 33}{space 2} .0389985{col 44}{space 1}    5.36{col 53}{space 3}0.000{col 61}{space 4} .1327578{col 74}{space 3} .2856293
{txt}{space 17}t7 {c |}{col 21}{res}{space 2} .4366133{col 33}{space 2}     .032{col 44}{space 1}   13.64{col 53}{space 3}0.000{col 61}{space 4} .3738944{col 74}{space 3} .4993321
{txt}{space 17}t8 {c |}{col 21}{res}{space 2} .4586674{col 33}{space 2} .0242733{col 44}{space 1}   18.90{col 53}{space 3}0.000{col 61}{space 4} .4110927{col 74}{space 3} .5062421
{txt}{space 17}t9 {c |}{col 21}{res}{space 2} .0882833{col 33}{space 2} .0234484{col 44}{space 1}    3.76{col 53}{space 3}0.000{col 61}{space 4} .0423252{col 74}{space 3} .1342414
{txt}{space 16}t10 {c |}{col 21}{res}{space 2} .0995571{col 33}{space 2} .0227384{col 44}{space 1}    4.38{col 53}{space 3}0.000{col 61}{space 4} .0549907{col 74}{space 3} .1441235
{txt}{space 16}t11 {c |}{col 21}{res}{space 2} .1183553{col 33}{space 2} .0199416{col 44}{space 1}    5.94{col 53}{space 3}0.000{col 61}{space 4} .0792706{col 74}{space 3} .1574401
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}     1,066{col 27}        .{col 38}-3420.155{col 49}    15{col 58} 6870.311{col 69} 6944.886
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. *Receiver random effects model
. xtpoisson dr_1_at_2_m Lmalaria_interp_2_m Lcid_per_trop_2_m Llog_trade_m Llog_gdppc_2_m Lln_cinc_2_m Ldemocracy_2_m Llog_distance_m t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12, i(cowcode_2)
{txt}note: t12 omitted because of collinearity

Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4875.3332}  
Iteration 1:{space 3}log likelihood = {res:-4875.2189}  
Iteration 2:{space 3}log likelihood = {res:-4875.2189}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -4356.518}  
Iteration 1:{space 3}log likelihood = {res:-4219.1758}  
Iteration 2:{space 3}log likelihood = {res:-4218.3591}  
Iteration 3:{space 3}log likelihood = {res:-4202.3074}  
Iteration 4:{space 3}log likelihood = {res: -4202.247}  
Iteration 5:{space 3}log likelihood = {res: -4202.247}  
{res}
{txt}Random-effects Poisson regression{col 49}Number of obs{col 67}={col 69}{res}     1,072
{txt}Group variable: {res}cowcode_2{col 49}{txt}Number of groups{col 67}={col 69}{res}       153

{txt}Random effects u_i ~ {res}Gamma{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}       7.0
{txt}{col 63}max{col 67}={col 69}{res}        11

{txt}{col 49}Wald chi2({res}17{txt}){col 67}={col 70}{res}  3455.39
{txt}Log likelihood  = {res} -4202.247{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        dr_1_at_2_m{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Lmalaria_interp_2_m {c |}{col 21}{res}{space 2}-.0775364{col 33}{space 2} .0306055{col 44}{space 1}   -2.53{col 53}{space 3}0.011{col 61}{space 4}-.1375221{col 74}{space 3}-.0175506
{txt}{space 2}Lcid_per_trop_2_m {c |}{col 21}{res}{space 2} .1631445{col 33}{space 2} .0566474{col 44}{space 1}    2.88{col 53}{space 3}0.004{col 61}{space 4} .0521176{col 74}{space 3} .2741715
{txt}{space 7}Llog_trade_m {c |}{col 21}{res}{space 2} .0009158{col 33}{space 2}  .000047{col 44}{space 1}   19.47{col 53}{space 3}0.000{col 61}{space 4} .0008236{col 74}{space 3} .0010079
{txt}{space 5}Llog_gdppc_2_m {c |}{col 21}{res}{space 2}-.0246862{col 33}{space 2} .0209773{col 44}{space 1}   -1.18{col 53}{space 3}0.239{col 61}{space 4}-.0658009{col 74}{space 3} .0164286
{txt}{space 7}Lln_cinc_2_m {c |}{col 21}{res}{space 2}  .149838{col 33}{space 2}  .012281{col 44}{space 1}   12.20{col 53}{space 3}0.000{col 61}{space 4} .1257677{col 74}{space 3} .1739084
{txt}{space 5}Ldemocracy_2_m {c |}{col 21}{res}{space 2} -.050292{col 33}{space 2} .0197123{col 44}{space 1}   -2.55{col 53}{space 3}0.011{col 61}{space 4}-.0889273{col 74}{space 3}-.0116566
{txt}{space 4}Llog_distance_m {c |}{col 21}{res}{space 2}-.3807164{col 33}{space 2} .0539816{col 44}{space 1}   -7.05{col 53}{space 3}0.000{col 61}{space 4}-.4865184{col 74}{space 3}-.2749144
{txt}{space 17}t2 {c |}{col 21}{res}{space 2} .2453284{col 33}{space 2} .0750088{col 44}{space 1}    3.27{col 53}{space 3}0.001{col 61}{space 4} .0983139{col 74}{space 3} .3923428
{txt}{space 17}t3 {c |}{col 21}{res}{space 2} .3430628{col 33}{space 2}  .052185{col 44}{space 1}    6.57{col 53}{space 3}0.000{col 61}{space 4}  .240782{col 74}{space 3} .4453435
{txt}{space 17}t4 {c |}{col 21}{res}{space 2} .3729429{col 33}{space 2} .0478595{col 44}{space 1}    7.79{col 53}{space 3}0.000{col 61}{space 4}   .27914{col 74}{space 3} .4667458
{txt}{space 17}t5 {c |}{col 21}{res}{space 2} .3204508{col 33}{space 2} .0398097{col 44}{space 1}    8.05{col 53}{space 3}0.000{col 61}{space 4} .2424252{col 74}{space 3} .3984764
{txt}{space 17}t6 {c |}{col 21}{res}{space 2} .2743238{col 33}{space 2} .0318191{col 44}{space 1}    8.62{col 53}{space 3}0.000{col 61}{space 4} .2119594{col 74}{space 3} .3366882
{txt}{space 17}t7 {c |}{col 21}{res}{space 2} .4982111{col 33}{space 2} .0266121{col 44}{space 1}   18.72{col 53}{space 3}0.000{col 61}{space 4} .4460522{col 74}{space 3} .5503699
{txt}{space 17}t8 {c |}{col 21}{res}{space 2} .5049508{col 33}{space 2}  .021246{col 44}{space 1}   23.77{col 53}{space 3}0.000{col 61}{space 4} .4633094{col 74}{space 3} .5465922
{txt}{space 17}t9 {c |}{col 21}{res}{space 2} .1307083{col 33}{space 2} .0214981{col 44}{space 1}    6.08{col 53}{space 3}0.000{col 61}{space 4} .0885727{col 74}{space 3} .1728439
{txt}{space 16}t10 {c |}{col 21}{res}{space 2}  .144764{col 33}{space 2} .0211789{col 44}{space 1}    6.84{col 53}{space 3}0.000{col 61}{space 4}  .103254{col 74}{space 3} .1862739
{txt}{space 16}t11 {c |}{col 21}{res}{space 2} .1546154{col 33}{space 2} .0192226{col 44}{space 1}    8.04{col 53}{space 3}0.000{col 61}{space 4} .1169398{col 74}{space 3}  .192291
{txt}{space 16}t12 {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (omitted)
{space 14}_cons {c |}{col 21}{res}{space 2}  6.74913{col 33}{space 2} .4814409{col 44}{space 1}   14.02{col 53}{space 3}0.000{col 61}{space 4} 5.805523{col 74}{space 3} 7.692737
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /lnalpha {c |}{col 21}{res}{space 2}-2.729534{col 33}{space 2}  .146557{col 61}{space 4} -3.01678{col 74}{space 3}-2.442287
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              alpha {c |}{col 21}{res}{space 2} .0652497{col 33}{space 2} .0095628{col 61}{space 4} .0489586{col 74}{space 3} .0869617
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test of alpha=0: {help j_chibar##|_new:chibar2(01) = }{res}1345.94{col 56}{txt}Prob >= chibar2 = {res}0.000
{txt}
{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}     1,072{col 27}        .{col 38}-4202.247{col 49}    19{col 58} 8442.494{col 69} 8537.062
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. **************
. **Table A.24**
. **************
. 
. *LR test statistics Malaria only model
. di chi2tail(1, -2*(-15012.691--6118.7462 ))
{res}0
{txt}
{com}. *calculate the likelihood ratio test statistic
. di "chi2(1) = " -2*(-15012.691--6118.7462 )
{res}chi2(1) = 17787.89
{txt}
{com}. 
. *LR test statistics Pooled model
. di chi2tail(1, -2*(-4875.219  --4234.072))
{res}7.96e-281
{txt}
{com}. *calculate the likelihood ratio test statistic
. di "chi2(1) = " -2*(-4875.219  --4234.072)
{res}chi2(1) = 1282.294
{txt}
{com}. 
. *LR test statistics Pooled (clustered se's) model
. di chi2tail(1, -2*(-4875.219 --4234.072))
{res}7.96e-281
{txt}
{com}. *calculate the likelihood ratio test statistic
. di "chi2(1) = " -2*(-4875.219 --4234.072)
{res}chi2(1) = 1282.294
{txt}
{com}. 
. *LR test statistics Receiver fixed effects model
. di chi2tail(1, -2*( -3420.155 --3275.997))
{res}1.157e-64
{txt}
{com}. *calculate the likelihood ratio test statistic
. di "chi2(1) = " -2*( -3420.155  --3275.997 )
{res}chi2(1) = 288.316
{txt}
{com}. 
. *LR test statistics Receiver random effects model
. di chi2tail(1, -2*(-4202.247  --4053.3419 ))
{res}9.882e-67
{txt}
{com}. *calculate the likelihood ratio test statistic
. di "chi2(1) = " -2*(-4202.247  --4053.3419)
{res}chi2(1) = 297.8102
{txt}
{com}. 
. *close log file
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Ben Bagozzi\Desktop\Replication Files\Tables_A23_to_A24.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}16 Apr 2021, 08:10:16
{txt}{.-}
{smcl}
{txt}{sf}{ul off}